气候变化领域集成服务门户(CCPortal): Active learning regularization increases clear sky retrieval rates for vegetation biophysical variables using Sentinel-2 data

CCPortal

DOI	10.1016/j.rse.2020.112241
	Active learning regularization increases clear sky retrieval rates for vegetation biophysical variables using Sentinel-2 data
	Djamai N.; Fernandes R.
发表日期	2021
ISSN	00344257
卷号	254
英文摘要	For typical cloud conditions, a clear sky retrieval rate (CSRR) >67% is required to meet the Global Climate Observing System temporal interval requirement of 10 days when mapping canopy biophysical variables (‘variables’). Physically based algorithms suitable for global mapping of variables using multispectral satellite imagery, e.g. the Simplified Level 2 Prototype Processor (SL2P), typically have a CSRR between 25% and 75%. An Active Learning Regularization (ALR) approach was developed to increase the CSRR rate while satisfying uncertainty requirements. A local calibration database for each variable was produced from representative valid SL2P estimates and associated Sentinel-2 Multispectral Instrument surface reflectance estimates. Predictors for each variable were developed by i) using Least Absolute Shrinkage and Selection Operator regression to select a subset of spectral vegetation indices (VIs) from a provided library, ii) removing outliers from the calibration database by trimming the conditional distribution of each variable given a VI, and iii) calibrating a non-linear regression predictor of the variable given the selected VIs using the trimmed database. ALR was applied to MSI imagery acquired over the Canadian Prairies during the 2016 and 2018 growing seasons and validated with in-situ data collected over 50 fields by the SMAPVEX16-MB campaign. The mean CSRR during the 2018 growing season was ~98% (~70%) for ALR (SL2P) for all canopy variables except FCOVER and ~ 98% for FCOVER using both ALR and SL2P. In comparison to SL2P, ALR had increased agreement rates with in-situ leaf area index (86% versus 79%) and fraction cover (96% versus 79%) but not canopy water content (35% versus 53%). Intercomparison with valid SL2P estimates from different MSI images acquired within ±2 days found that 90% [±5%] of ALR estimates fell within the uncertainty of the valid estimates. These findings support the hypothesis that, over croplands, ALR significantly increases CSRR over SL2P without appreciably increasing uncertainty for variables retrieved by SL2P within thematic performance requirements. © 2020 The Author(s)
英文关键词	Active learning regularization; Canopy biophysical variables; Clear-sky retrieval rate; Sentinel-2; SL2P; Vegetation indices
语种	英语
scopus关键词	Calibration; Distributed database systems; Mapping; Satellite imagery; Vegetation; Bio-physical variables; Conditional distribution; Global climate observing systems; Least absolute shrinkage and selection operators; Multispectral instruments; Multispectral satellite imagery; Performance requirements; Spectral vegetation indices; Uncertainty analysis; algorithm; biophysics; calibration; clear sky; database; growing season; in situ measurement; machine learning; mapping method; satellite data; satellite imagery; Sentinel; spectral analysis; spectral reflectance; vegetation dynamics; Canada; Prairie Provinces
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178990
作者单位	Canada Centre for Remote Sensing, Natural Resources Canada, 560 Rochester Street, Ottawa, ON K1A 0E4, Canada
推荐引用方式 GB/T 7714	Djamai N.,Fernandes R.. Active learning regularization increases clear sky retrieval rates for vegetation biophysical variables using Sentinel-2 data[J],2021,254.
APA	Djamai N.,&Fernandes R..(2021).Active learning regularization increases clear sky retrieval rates for vegetation biophysical variables using Sentinel-2 data.Remote Sensing of Environment,254.
MLA	Djamai N.,et al."Active learning regularization increases clear sky retrieval rates for vegetation biophysical variables using Sentinel-2 data".Remote Sensing of Environment 254(2021).